Historically, robot behaviors have been created for specific tasks and applications. These behaviors have generally been reinvented time and again for different robots and different applications. There has been no sustained attempt to provide a kernel of basic robot competence and decision making that can be used to bootstrap development across many different applications.
Some architectures have been proposed that provide a generic application programming interface (API) for querying various sensors and commanding various actuators; however, many of these architectures have been limited to raw inputs and outputs rather than provide the intelligence and behavior to a robot. As a result, the behavior functionality created for one robot may not be easily ported to new robots. Other architectures have been proposed to allow limited behaviors to port across different robot platforms, but these have generally been limited to specific low-level control systems.
The problem with robots today is that they are not very bright. Current robot “intelligence” is really just a grab-bag of programmed behaviors to keep mobile robots from doing stupid things, like getting stuck in corners or running into obstacles. The promise of wireless robots is that they can be sent into remote situations that are too difficult or dangerous for humans. The reality is that today's robots generally lack the ability to make any decisions on their own and rely on continuous guidance by human operators watching live video from on-board cameras.
Most commercial robots operate on a master/slave principle. A human operator completely controls the movement of the robot from a remote location using robot-based sensors such as video and Global Positioning System (GPS). This setup often requires more than one operator per robot to navigate around obstacles and achieve a goal. As a result, very skilled operators may be necessary to reliably direct the robot. Furthermore, the intense concentration needed for controlling the robot can detract from achieving mission goals.
Although it has been recognized that there is a need for adjustable autonomy, robot architectures currently define levels of autonomy that are only selectable by the user. As a result, performance of the robot and its human controller is often dependent on the human controller's understanding of the robot's autonomy levels and when the various levels are appropriate.
One common task for robots is to perform threat assessments in environments that may be too dangerous for humans. A typical threat assessment in a hazardous environment requires teams of highly trained personnel and may require extensive personal protective equipment (PPE), decontamination facilities, rescue teams, and other preparations. The PPE that may be needed can range from anti-contamination (anti-C) boots and gloves to full-HAZMAT (HAZardous MATerial) suits with supplied breathing air. Simply moving down range in a respirator or with heavy oxygen tanks and anti-Cs or a HAZMAT suit is difficult. Multiple bulky instruments may be required to detect and assess varied potential hazards. The instruments themselves may need to be wrapped adding to their weight and making control and interpretation difficult, especially when wearing gloves and a respirator or supplied air. The time in which an assessment team may be able to stay in the environment can be limited by ambient temperature, workload, and various other external factors. These issues can severely limit the responder's situational awareness of the environment and developing hazards and make it difficult to assess the hazards, identify safety boundaries, and report the results for an effective response. Significant manpower may be needed, such as a backup two-man team if for rescue, or to continue assessment, perform decontamination, or other support.
When returning from a down-range exploration, responders generally provide a map of the area including where the hazards are located and at what levels the hazards were recorded. This requires the responder to either remember the information or transmit the information back to the main base where other responders can create the map. In order to effectively perform the full mission, responders are also thoroughly trained, which can take significant time to learn the equipment and procedures. Even with training, the process of manually mapping an area is extremely time consuming and the results, especially in large, complex environments, are often inaccurate or incomplete. Furthermore, empirical studies have shown that differing levels of training is often evidenced in performance of the task with current state-of-the-art robotics.
While there are tactics, techniques, procedures, and technology to perform threat assessments, the approaches are constrained by human vulnerability to the environment, human error, human communication protocols, and human memory. Ideally, humans would be able to gather and map information about the hazardous environment while stationed a safe distance from the hazard. Robots are sometimes used for this approach. However, conventionally these hazard robot applications operate in a teleoperation mode where a human user directs movements of the robot. In addition, localization and visualization of specific hazard levels in the environment has been limited.
Therefore, there is a need for systems and methods that provide a more autonomous level of decision making when seeking out and localizing hazardous materials, as well as intuitive visualizations of the environment and hazard intensities detected therein.